# Robust multi-objective learning with mentor feedback

## Author(s): Agarwal, A; Badanidiyuru, A; Dudík, M; Schapire, RE; Slivkins, A

To refer to this page use: http://arks.princeton.edu/ark:/88435/pr1rv7p
 Abstract: © 2014 A. Agarwal, A. Badanidiyuru, M. Dudík, R.E. Schapire & A. Slivkins. We study decision making when each action is described by a set of objectives, all of which are to be maximized. During the training phase, we have access to the actions of an outside agent ("mentor"). In the test phase, our goal is to maximally improve upon the mentor's (unobserved) actions across all objectives. We present an algorithm with a vanishing regret compared with the optimal possible improvement, and show that our regret bound is the best possible. The bound is independent of the number of actions, and scales only as the logarithm of the number of objectives. Publication Date: 1-Jan-2014 Citation: Agarwal, A, Badanidiyuru, A, Dudík, M, Schapire, RE, Slivkins, A. (2014). Robust multi-objective learning with mentor feedback. Journal of Machine Learning Research, 35 (726 - 741 ISSN: 1532-4435 EISSN: 1533-7928 Pages: 726 - 741 Type of Material: Conference Article Journal/Proceeding Title: Journal of Machine Learning Research Version: Final published version. This is an open access article.